Transcript
Tens of millions of people suffer from Parkinson’s disease and epilepsy. The resulting human and financial cost of is staggering. Fortunately, researchers have now been able to combine low-power chip design, machine learning algorithms, and soft implantable electrodes to produce NeuralTree, a closed loop neuromodulation system-on-a-chip that can detect and alleviate symptoms of Parkinson’s disease and epilepsy.
NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm. It’s the first time we’ve been able to integrate such a complex, yet energy-efficient neural interface for seizure or tremor detection, as well as for multiclass tasks such as finger movement classification for neuro-prosthetic applications. The results of this research were presented at the 2022 IEEE International Solid-State Circuits Conference and published in the IEEE Journal of Solid-State Circuits.
NeuralTree functions by extracting neural biomarkers from brain waves. It then classifies the signals and indicates whether they herald an impending epileptic seizure or Parkinsonian tremor. If a symptom is detected, a neurostimulator — also located on the chip — is activated, sending an electrical pulse to block it. This is the first demonstration of Parkinsonian tremor detection with an onchip classifier.
The researchers explain that NeuralTree’s unique design gives the system an unprecedented degree of efficiency and versatility compared to the state-of-the-art. The chip boasts 256 input channels, compared to 32 for previous machine-learning-embedded devices; this allows more high-resolution data to be processed on the implant. The chip’s extremely small size gives it great potential for scalability to more channels. And the integration of an ‘energy-aware’ learning algorithm makes NeuralTree highly energy efficient.
The chip’s machine learning algorithm was trained on datasets from both epilepsy and Parkinson’s disease patients, and accurately classified pre-recorded neural signals from both categories. As a next step, the team is interested in enabling on-chip algorithmic updates to keep up with the evolution of neural signals. That’s important because neural signals change; so, over time the performance of a neural interface will decline unless updated. One way to address that is to enable on-chip updates, or algorithms that can update themselves.
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